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專欄 - 向Anne提問

數(shù)據(jù)科學家緊俏,數(shù)學背景并非必須

Anne Fisher 2013年05月16日

Anne Fisher為《財富》雜志《向Anne提問》的專欄作者,這個職場專欄始于1996年,幫助讀者適應經(jīng)濟的興衰起落、行業(yè)轉(zhuǎn)換,以及工作中面臨的各種困惑。
大數(shù)據(jù)領域的發(fā)展實在太快了,盡管企業(yè)雇主還沒搞清楚到底應該招納何種大數(shù)據(jù)人才,他們還是在瘋狂地招兵買馬。如果你學了數(shù)據(jù)學,并且學得很好,那么在你畢業(yè)以后,肯定會有一份工作等著你。

????另一方面,你需要有多少數(shù)學和電腦技能,還取決于公司想讓你承擔的角色。詹寧斯表示:“如果你不是一個純粹搞數(shù)學的人,或者不是一個專業(yè)的編程人員,那也沒關系,因為你可以和那樣的人在同一支團隊里工作。除了量化分析方面以外,我們還非常需要具有求知和好奇天性的人,以及能夠指出業(yè)務上的問題并且能與客戶溝通的人。如果你能幫助一個客戶理解一個問題的癥結(jié)所在,然后把數(shù)據(jù)學家的解決方案用直白淺顯的語言解釋給他們,那么你就會成為明星員工?!?/p>

????獵頭公司DHR國際的美國西部區(qū)負責人亞當?查爾森也同意詹寧斯的看法,他表示:“產(chǎn)品經(jīng)理的工作就是大數(shù)據(jù)開始和結(jié)束的地方?!辈闋柹呀?jīng)幫助費埃哲、益百利(Expeiran)、谷歌(Google)、惠普(HP)、Paypal和許多其它企業(yè)招聘到了大數(shù)據(jù)人才。

????查爾森解釋說,產(chǎn)品經(jīng)理的工作,是要“與客戶共同發(fā)現(xiàn)挑戰(zhàn),把問題告知量化分析人員,由量化分析人員建立一個數(shù)學模型來解決這項挑戰(zhàn)。然后產(chǎn)品經(jīng)理回到客戶那里,解釋量化分析團隊的發(fā)現(xiàn)。最重要的是策略、創(chuàng)造性和溝通能力。如果你這幾點都有的話,數(shù)學或編程技能并不是那么重要?!?/p>

????查爾森還指出,如今數(shù)據(jù)分析師和產(chǎn)品經(jīng)理的晉升空間很大,甚至可能晉升到最高管理層。他表示:“現(xiàn)在很多的企業(yè)都需要一個‘首席數(shù)據(jù)官’。自從上世紀90年代出現(xiàn)了‘首席信息官’以來,這還是我們第一次見到大量企業(yè)涌現(xiàn)出一個新的“首席”職位?!?/p>

????善意提醒正在挑選大學和專業(yè)的學生:你是否考慮過學習數(shù)據(jù)學?卡耐基梅隆大學(Carnegie Mellon University)、麻省理工學院(MIT)以及北卡羅來納大學(University of North Carolina)等都有非常知名的數(shù)據(jù)學專業(yè),還有許多其他大學也相繼開設了數(shù)據(jù)學課程。另外美國的運籌學和管理學研究學會(INFORMS)還提供了一個全新的大數(shù)據(jù)人才資質(zhì)認證項目,能夠提供標準化的大數(shù)據(jù)領域的資格證書。

????查爾森表示:“預測分析是一個全新事物,就在五到七年前,還沒有人在大學以外的地方做這件事。但是現(xiàn)在,它已經(jīng)步入了主流。如果你學了數(shù)據(jù)學,并且學得很好,那么在你畢業(yè)以后,肯定會有一份工作等著你?!边@種話當今的大學生已經(jīng)很難聽到了,更不用說是為他們付學費的父母了。(財富中文網(wǎng))

????譯者:樸成奎

????But on the other hand, how much math and computer expertise you need depends on the role an employer is trying to fill at any given moment. "If you're not a pure-math person or an expert programmer, that's all right, because you can be on a team with people who are," Jennings says. "Apart from the quantitative aspects, there is a tremendous need for people who are inquisitive by nature, who are curious, and who have a talent for figuring out business problems and communicating with clients. If you can help a client understand the crux of a situation and then translate the data scientists' solution to it into plain English, you'll be a star."

????Adam Charlson agrees. "The job of product manager is where Big Data begins and ends," he says. Head of Western U.S. recruiting for executive search firm DHR International, Charlson has helped FICO, Experian, Google (GOOG), HP (HPQ), PayPal, and many others fill Big Data jobs.

????A product manager, he explains, "works with the client to identify a challenge and then takes it to the quant jocks, who build a mathematical model to address it. Then the product manager goes back to the client and explains the findings. It's about strategy, creativity, and the ability to communicate. If you have all that, math or programming skills aren't so important."

????These days, Charlson notes, the career path for data scientists and product managers can lead all the way to the C-suite. "There is enormous growth in demand for chief data officers at companies," he says. "It's the first time we've seen a new C-suite title added in large numbers since the wave of chief information officers back in the '90s."

????Note to students who are trying to choose a college or a major: Have you thought about data science? Carnegie Mellon University and M.I.T. have well-established data science degree programs, as does the University of North Carolina, and many other colleges are adding them. There is also a brand new certification program in Big Data, offered by analytics professional association INFORMS, that is designed to provide a standardized credential in the field.

????"Predictive analytics is such a new thing that, five or seven years ago, nobody outside of academia was even doing it," Charlson says. "But now, it's gone mainstream. If you study data science, and you're good at it, there will be a job for you after graduation, guaranteed." That's something today's college students, not to mention their tuition-paying parents, rarely hear.

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